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2016

Feature Importance Measure for Non-linear Learning Algorithms

Vidovic, Marina M. -C., Görnitz, Nico, Müller, Klaus-Robert et al.

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Complex problems may require sophisticated, non-linear learning methods such as kernel machines or deep neural networks to achieve state of the art prediction accuracies.

However, high prediction accuracies are not the only objective to consider when solving problems using machine learning.
Instead, particular scientific applications require some explanation of the learned prediction function.
Unfortunately, most methods do not come with out of the box straight forward interpretation.

Feature Importance Measure for Non-linear Learning Algorithms · Around

Built on

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
B. Schölkopf and A. J. Smola · 2002
Earlier work this paper cites.
Measuring Statistical Dependence with Hilbert-Schmidt Norms
A. Gretton, O. Bousquet, A. Smola, and B. Schölkopf · 2005
Earlier work this paper cites.
Improving the Caenorhabditis elegans genome annotation using machine learning
G. Rätsch, S. Sonnenburg, J. Srinivasan, H. Witte, K. R. Müller, R. J. Sommer, and B. Schoelkopf · 2007
Earlier work this paper cites.
Accurate splice site prediction using support vector machines
S. Sonnenburg, G. Schweikert, P. Philips, J. Behr, and G. Rätsch · 2007
Earlier work this paper cites.

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Then

Evaluating the visualization of what a deep neural network has learned
Original
W. Samek, A. Binder, G. Montavon, S. Bach, and K.-R. Müller · 2015
Later among the works it cites.
Opening the Black Box: Revealing Interpretable Sequence Motifs in Kernel-Based Learning Algorithms
M. M.-C. Vidovic, N. Görnitz, K.-R. Müller, G. Rätsch, and M. Kloft · 2015
Later among the works it cites.
SVM2Motif — Reconstructing Overlapping DNA Sequence Motifs by Mimicking an SVM Predictor
M. M.-C. Vidovic, N. Görnitz, K.-R. Müller, G. Rätsch, and M. Kloft · 2015
Later among the works it cites.

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